arXiv:2603.18088v1 Announce Type: cross Abstract: Constraints are essential for stabilizing reinforcement learning fine-tuning (RFT) and preventing degenerate outputs, yet they inherently conflict with the optimization objective because stronger constraints limit the ability of a fine-tuned model to discover better solutions. We propose textitdynamic constraints that resolve this tension by adapting to the evolving capabilities of […]
Agentic LLM Framework for Adaptive Decision Discourse
arXiv:2502.10978v2 Announce Type: replace Abstract: Effective decision-making in complex systems requires synthesizing diverse perspectives to address multifaceted challenges under uncertainty. This study introduces an agentic Large Language Models (LLMs) framework for simulating decision discourse – the deliberative process through which actionable strategies are collaboratively developed. Unlike traditional decision-support tools, this framework simulates diverse stakeholder personas, […]
Training-Only Heterogeneous Image-Patch-Text Graph Supervision for Advancing Few-Shot Learning Adapters
arXiv:2603.18101v1 Announce Type: cross Abstract: Recent adapter-based CLIP tuning (e.g., Tip-Adapter) is a strong few-shot learner, achieving efficiency by caching support features for fast prototype matching. However, these methods rely on global uni-modal feature vectors, overlooking fine-grained patch relations and their structural alignment with class text. To bridge this gap without incurring inference costs, we […]
ClawTrap: A MITM-Based Red-Teaming Framework for Real-World OpenClaw Security Evaluation
arXiv:2603.18762v1 Announce Type: cross Abstract: Autonomous web agents such as textbfOpenClaw are rapidly moving into high-impact real-world workflows, but their security robustness under live network threats remains insufficiently evaluated. Existing benchmarks mainly focus on static sandbox settings and content-level prompt attacks, which leaves a practical gap for network-layer security testing. In this paper, we present […]
VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models
arXiv:2603.18113v1 Announce Type: cross Abstract: As large language models (LLMs) increasingly shape content generation, interaction, and decision-making across the Web, aligning them with human values has become a central objective in trustworthy AI. This challenge becomes even more pronounced when aligning multiple, potentially conflicting human values. Although recent approaches, such as reward reweighting, prompt-based supervised […]
Security, privacy, and agentic AI in a regulatory view: From definitions and distinctions to provisions and reflections
arXiv:2603.18914v1 Announce Type: cross Abstract: The rapid proliferation of artificial intelligence (AI) technologies has led to a dynamic regulatory landscape, where legislative frameworks strive to keep pace with technical advancements. As AI paradigms shift towards greater autonomy, specifically in the form of agentic AI, it becomes increasingly challenging to precisely articulate regulatory stipulations. This challenge […]
Steering Awareness: Detecting Activation Steering from Within
arXiv:2511.21399v3 Announce Type: replace-cross Abstract: Activation steering — adding a vector to a model’s residual stream to modify its behavior — is widely used in safety evaluations as if the model cannot detect the intervention. We test this assumption, introducing steering awareness: a model’s ability to infer, during its own forward pass, that a steering […]
Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions
arXiv:2603.10651v2 Announce Type: replace-cross Abstract: Task and Motion Planning combines high-level task sequencing (what to do) with low-level motion planning (how to do it) to generate feasible, collision-free execution plans. However, in many real-world domains, such as automated warehouses, tasks are predefined, shifting the challenge to if, when, and how to execute them safely and […]
Discovering What You Can Control: Interventional Boundary Discovery for Reinforcement Learning
arXiv:2603.18257v1 Announce Type: cross Abstract: Selecting relevant state dimensions in the presence of confounded distractors is a causal identification problem: observational statistics alone cannot reliably distinguish dimensions that correlate with actions from those that actions cause. We formalize this as discovering the agent’s Causal Sphere of Influence and propose Interventional Boundary Discovery IBD, which applies […]
Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering
arXiv:2603.18166v1 Announce Type: new Abstract: Crowd trajectory prediction plays a crucial role in public safety and management, where it can help prevent disasters such as stampedes. Recent works address the problem by predicting individual trajectories and considering surrounding objects based on manually annotated data. However, these approaches tend to overlook dense crowd scenarios, where the […]
Evaluating Hallucinations in Audio-Visual Multimodal LLMs with Spoken Queries under Diverse Acoustic Conditions
arXiv:2510.08581v2 Announce Type: replace-cross Abstract: Hallucinations in multimodal models have been extensively studied using benchmarks that probe reliability in image-text query settings. However, the effect of spoken queries on multimodal hallucinations remains largely unexplored, despite the growing role of voice interfaces. In this paper, we introduce a systematic pipeline that converts existing multimodal hallucination benchmarks […]
D5P4: Partition Determinantal Point Process for Diversity in Parallel Discrete Diffusion Decoding
arXiv:2603.19146v1 Announce Type: new Abstract: Discrete diffusion models are promising alternatives to autoregressive approaches for text generation, yet their decoding methods remain under-studied. Standard decoding methods for autoregressive models, such as beam search, do not directly apply to iterative denoising, and existing diffusion decoding techniques provide limited control over in-batch diversity. To bridge this gap, […]